User experince in cycling - a study on emotional term of beauty and confidence

In the 1980s, Donald Norman’s research laboratory originated the term ‘user-centered design’ putting users at the priority. From then, fulfilling users’ needs become the most important goal of designs. In the past, designer only concerned about the basic needs of user and mainly focused on the usabi...

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Bibliographic Details
Main Author: Wang, Qi
Other Authors: Chen Chun-Hsien
Format: Final Year Project
Language:English
Published: 2015
Subjects:
Online Access:http://hdl.handle.net/10356/64567
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Institution: Nanyang Technological University
Language: English
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Summary:In the 1980s, Donald Norman’s research laboratory originated the term ‘user-centered design’ putting users at the priority. From then, fulfilling users’ needs become the most important goal of designs. In the past, designer only concerned about the basic needs of user and mainly focused on the usability or functionality of a product. As time goes on, the concept of emotional design was brought up and valued by more and more designers. Emotion can be quantified by a variety of methods, the most popular concepts is Kansei Engineering brought by Mitsuo Nagamachi. Kansei Engineering can link the customer's emotional feeling to the properties and characteristics of a product. By paying attention to the corresponding properties and characteristics, the product can bring better feelings and more satisfactory to users. In this Final Year Project, Kansei Engineering was applied in the studying on emotions of cyclist during cycling activity. One-to-one interviews were conducted and conceptual modules of the cycling activity were draw for groups of cyclist with different gender and experience level. From the interview, hypothesis was proposed and the significant emotion terms were concluded. Eye tracking experiment was designed based on these emotion terms, to identifying the correlations between these terms and the component and characters of bicycles. The resulted eye movement data got from the eye tracking experiment was analyzed by statistical approach using SPSS software to validate the hypothesis. In-depth analysis and discussions were done on the classification results.